Dynamic energy-accuracy trade-off using stochastic computing in deep neural networks. - Kyounghoon Kim; Jungki Kim; Joonsang Yu; Jungwoo Seo; Jongeun Lee; Kiyoung Choi (DAC, 2016)

A new approach to binarizing neural networks. - Jungwoo Seo; Joonsang Yu; Jongeun Lee; Kiyoung Choi (ISOCC, 2016)

Accurate and Efficient Stochastic Computing Hardware for Convolutional Neural Networks. - Joonsang Yu; Kyounghoon Kim; Jongeun Lee; Kiyoung Choi (ICCD, 2017)

A new stochastic mutiplier for deep neural networks. - Subin Huh; Joonsang Yu; Kiyoung Choi (ISOCC, 2017)

Hybrid spiking-stochastic Deep Neural Network. - Heesu Kim; Joonsang Yu; Kiyoung Choi (VLSI-DAT, 2017)

Efficient Low-Cost Fault-Localization and Self-Repairing Radix-2 Signed-Digit Adders Applying the Self-Dual Concept. - Hossein Moradian; Jeong-A Lee; Joonsang Yu (J. Signal Process. Syst., 2017)

Tapered-Ratio Compression for Residual Network. - Sungbum Kang; Joonsang Yu; Kiyoung Choi (ISOCC, 2018)

Network Recasting: A Universal Method for Network Architecture Transformation. - Joonsang Yu; Sungbum Kang; Kiyoung Choi (AAAI, 2019)

Acceleration of DNN Backward Propagation by Selective Computation of Gradients. - Gunhee Lee; Hanmin Park; Namhyung Kim; Joonsang Yu; Sujeong Jo; Kiyoung Choi (DAC, 2019)

DANCE: Differentiable Accelerator/Network Co-Exploration. - Kanghyun Choi; Deokki Hong; Hojae Yoon; Joonsang Yu; Youngsok Kim; Jinho Lee (DAC, 2021)

It's All In the Teacher: Zero-Shot Quantization Brought Closer to the Teacher. - Kanghyun Choi; Hyeyoon Lee; Deokki Hong; Joonsang Yu; Noseong Park; Youngsok Kim; Jinho Lee (CVPR, 2022)

Enabling hard constraints in differentiable neural network and accelerator co-exploration. - Deokki Hong; Kanghyun Choi; Hyeyoon Lee; Joonsang Yu; Noseong Park; Youngsok Kim; Jinho Lee (DAC, 2022)

NN-LUT: neural approximation of non-linear operations for efficient transformer inference. - Joonsang Yu; Junki Park; Seongmin Park 003; Minsoo Kim; Sihwa Lee; Dong Hyun Lee; Jungwook Choi (DAC, 2022)

Rediscovery of the Effectiveness of Standard Convolution for Lightweight Face Detection. - Joonhyun Jeong; Beomyoung Kim; Joonsang Yu; Youngjoon Yoo (CoRR, 2022)

Pipe-BD: Pipelined Parallel Blockwise Distillation. - Hongsun Jang; Jaewon Jung 001; Jaeyong Song; Joonsang Yu; Youngsok Kim; Jinho Lee (DATE, 2023)

GeNAS: Neural Architecture Search with Better Generalization. - Joonhyun Jeong; Joonsang Yu; Geondo Park; Dongyoon Han; Youngjoon Yoo (IJCAI, 2023)

Gaussian Mixture Proposals with Pull-Push Learning Scheme to Capture Diverse Events for Weakly Supervised Temporal Video Grounding. - Sunoh Kim; Jungchan Cho; Joonsang Yu; Youngjoon Yoo; Jin Young Choi 002 (AAAI, 2024)

ECLIPSE: Efficient Continual Learning in Panoptic Segmentation with Visual Prompt Tuning. - Beomyoung Kim; Joonsang Yu; Sung Ju Hwang (CVPR, 2024)

Cost-effective IA-EML for 5G-advanced and 6G Fronthaul Networks. - Seungchul Lee; Namje Kim; Yonghan Kim; Miran Park; Jun-Hwan Shin; O-Kyun Kwon; Kihong Yoon; Mihee Hwang; Joonsang Yu (ICTC, 2024)

400G cost-effective EML for B5G/6G Fronthaul Network. - Seungchul Lee; Namje Kim; Miran Park; Kihong Yoon; Mihee Hwang; Joonsang Yu; Sangho Lee; O-Kyun Kwon (OFC, 2024)

EResFD: Rediscovery of the Effectiveness of Standard Convolution for Lightweight Face Detection. - Joonhyun Jeong; Beomyoung Kim; Joonsang Yu; Youngjoon Yoo (WACV, 2024)

Towards Label-Efficient Human Matting: A Simple Baseline for Weakly Semi-Supervised Trimap-Free Human Matting. - Beomyoung Kim; Myeong-Yeon Yi; Joonsang Yu; Young Joon Yoo; Sung Ju Hwang (CoRR, 2024)

HyperCLOVA X Technical Report. - Kang Min Yoo; Jaegeun Han; Sookyo In; Heewon Jeon; Jisu Jeong; Jaewook Kang; Hyunwook Kim; Kyung-Min Kim; Munhyong Kim; Sungju Kim; Donghyun Kwak; Hanock Kwak; et al. (CoRR, 2024)

ZIM: Zero-Shot Image Matting for Anything. - Beomyoung Kim; Chanyong Shin; Joonhyun Jeong; Hyungsik Jung; Se-Yun Lee; Sewhan Chun; Dong-Hyun Hwang; Joonsang Yu (CoRR, 2024)